Models, systems, networks in economics, engineering, nature and society - M.I. Krevskiy; A.S. Bozhday (Модели, системы, сети в экономике, технике, природе и обществе)
Background. Process Мining is a young, dynamically developing field of data science. This data-driven approach to process analytics has already proven its effectiveness and is increasingly gaining popularity in large companies. The purpose of this article is a review analysis of existing theoretical and practical solutions in the field of business process management in order to develop useful recommendations that will facilitate the creation of domestic intelligent systems for managing organizational processes based on process data analytics. Materials and methods. The paper examines the materials of international conferences devoted to process analytics. The methods of assessing the quality of stochastic process models, alignment of business process paths, predictive analysis, event data analysis and methods of advisory support of business processes are considered. Results. In the course of the work, modern approaches to each type of task in Process Mining are analyzed. The application of the ideas and methods discussed in this review in practice will significantly expand the functionality of process analytics systems using only classical Process Mining methods, predict the progress of the process with high accuracy, avoid information loss, ensure transparency and validity of the proposed changes. Conclusions. The currently existing scientific and technological solutions of Process Mining make it possible to effectively automate most of the tasks business analysts face in both the public sector and private business. However, these solutions are far from complete and leave room for the development and improvement of analytical tools for process and predictive analytics.